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Publication# On raw inertial measurements in dynamic networks

Abstract

Dynamic Networks have been introduced in the literature to solve multi-sensor fusion problems for navigation and mapping. They have been shown to outperform conventional methods in challenging scenarios, such as corridor mapping or self-calibration. In this work we investigate the problem of how raw inertial readings can be fused with GNSS position observations in Dynamic Networks (DN) with the goal of i) limiting the number of unknowns in the estimation problem and ii) improving the conditioning of the normal equations arising in least-squares adjustments in the absence of spatial constraints (e.g., image observations). For that we propose a modified version of the well known IMU-preintegration method, accounting for a non-constant gravity model, the Earth rotation and the apparent Coriolis force, and we compare it with the conventional DN formulation in a emulated scenario. This consists of a fixed-wing UAV flying four times over a 2 km long corridor.

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In physics, the Coriolis force is an inertial or fictitious force that acts on objects in motion within a frame of reference that rotates with respect to an inertial frame. In a reference frame with clockwise rotation, the force acts to the left of the motion of the object. In one with anticlockwise (or counterclockwise) rotation, the force acts to the right. Deflection of an object due to the Coriolis force is called the Coriolis effect.

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